Subsetting brain data

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.

BACKGROUND

This specification relates to processing data related to the brain of apatient, e.g., functional magnetic resonance imaging (MM) data and/ordiffusion tractography data.

Brain functional connectivity data characterizes, for each of one ormore pairs of locations within the brain of a patient, the degree towhich brain activity in the pair of locations is correlated.

One can gather data related to the brain of the patient by obtaining andprocessing images of the brain of the patient, e.g., using magneticresonance imaging (MM), diffusion tractography (DT) imaging, orfunctional MRI imaging (fMRT). Diffusion tensor imaging uses magneticresonance images to measure diffusion of water in a human brain. One canuse the measured diffusion to generate images of neural tracts andcorresponding white matter fibers of the subject brain.

Data related to the brain of a single patient can be highly complex andhigh-dimensional, and therefore difficult for a clinician to manuallyinspect and parse, e.g., to plan a surgery or diagnose the patient for abrain disease or mental disorder. For example, a correlation matrix,e.g., a correlation matrix of fMRI data, of the brain of a patient canbe a matrix with hundreds of thousands or millions of elements.

SUMMARY

This specification relates to determining a subset of brain data of apatient that is clinically relevant for the patient, e.g., a subset ofbrain data that is relevant to a diagnosable condition and/or upon whicha user/clinician can take an action, and presenting the subset of thebrain data to a user. For example, the system can determine a subset ofthe brain data that is clinically relevant for a particular braindisease or mental disorder, one or more symptoms, or a particular brainsurgery that the patient may undergo. As a particular example, thesystem can determine a subset of fMRI data, e.g., correlation matrices,and diffusion tractography data characterizing the brain of the patient.

In this specification, brain data can be any data characterizing thebrain of a patient. For example, brain data can include one or both ofi) direct measurement data of the brain of the patient, e.g., images ofthe brain collected using brain imaging techniques, or ii) data that hasbeen derived or generated from initial measurement data of the brain ofthe patient, e.g., correlation matrices.

In order to determine the subset of the brain data, a system can providea series of prompts to the user corresponding to clinically-relevantquestions about the patient. Upon receiving a response to a particularprompt, the system can either determine the subset of the brain dataaccording to the received responses to the particular prompt and theprevious prompts, or determine a next prompt to provide to the user.That is, the sequence of prompts can be dynamic according to theresponses provided by the user.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. As discussed above, a set of brain datacharacterizing the brain of a single patient can often be incrediblylarge and complicated, and thus it can be difficult and time consumingfor a user to extract useful information from the set of brain data.

Using techniques described in this specification, a system can determinea subset of the brain data that is significantly smaller than the entireset of brain data, e.g., 1/100^(th), 1/1,000^(th), or 1/10,000^(th) thesize of the entire set of brain data where the entire set represents allor most of the connections represented in a connectome (a map ofneuronal connections in the brain). Therefore, the user is not forced tosearch through and analyze a large amount of data that is not relevantto the particular use case of the user, e.g., a particular disease orbrain surgery. Rather, the system provides to the user the subset of thebrain data that has been determined, either through clinical studies orby one or more machine learning models, to be clinically relevant forthe particular use case.

Using techniques described in this specification, a system can determinea series of prompts that are to be provided to the user that allow theuser to quickly navigate to a target subset of the data. As particularexamples, the series of prompts can include only 2, 5, or 10 prompts towhich the user can respond with relative ease. Therefore, the amount oftime that a user must spend to discover the portion of the brain datathat is useful to the user can be drastically reduced, resulting inimproved outcomes for patients, users and/or clinicians, especially wheneffective care requires time sensitive investigations.

In some implementations, the system can further recommend one or moremachine learning models for processing the determined subset of thebrain data. The recommended machine learning models can be determinedaccording to the same series of prompts by which the subset of braindata was determined, and can generate an output that is clinicallyrelevant for the user. As a particular example, a recommended machinelearning model can process the determined subset of the brain data togenerate a prediction for whether the patient has a particular braindisease that was identified by the user in the responses to the seriesof prompts. Therefore, the system can provide immediate access to themost relevant machine learning models to the user, without requiring theuser to select expressly the machine learning model(s) relevant to thecontext. This can be particularly advantageous when the system has alarge library of machine learning models that may be difficult or timeconsuming for a user to look through in order to select a particularmachine learning model.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B are block diagrams that illustrate an examplecomputer system.

FIG. 2 illustrates an example introductory series of prompts.

FIG. 3 illustrates an example series of prompts related to brain tumors.

FIG. 4 illustrates an example series of prompts related to dementia.

FIG. 5 illustrates an example series of prompts related to depression.

FIG. 6 illustrates an example series of prompts related to epilepsy.

FIG. 7 illustrates an example graphical interface for presenting asubset of brain data.

FIG. 8 is a flowchart of an example process for determining a subset ofbrain data.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This specification describes a system that can determine a subset ofbrain data of a patient that is clinically relevant for the patient. Inthis specification, a set of data is “clinically relevant” if the datacan be presented as an answer to a question that a trained clinicianmight ask in clinical practice treating patients, e.g., a question askedby a clinician in order to treat a patient with a specific diseasemeasured in the data.

For example, the system can determine a subset of brain data based atleast in part on clinical observation input, where the clinicalobservation is of the individual whose brain data is in question. Thesystem can determine the subset of data using responses to a series ofprompts provided to a user, e.g., a clinician. The system can thenpresent the subset of the brain data to the user in a graphicalinterface. In some implementations, the system can further determine oneor more machine learning models for processing the determined subset ofdata to generate clinically-relevant model outputs.

FIGS. 1A and 1B are block diagrams of a general-purpose computer system200 upon which one can practice arrangements described in thisspecification. The following description is directed primarily to acomputer server module 201. However, the description applies equally orequivalently to one or more remote terminals 268.

As seen in FIG. 1A, the computer system 200 includes: the servercomputer module 201; input devices such as a keyboard 202, a pointerdevice 203 (e.g., a mouse), a scanner 226, a camera 227, and amicrophone 280; and output devices including a printer 215, a displaydevice 214 and loudspeakers 217. An external Modulator-Demodulator(Modem) transceiver device 216 may be used by the computer server module201 for communicating to and from the remote terminal 268 over acomputer communications network 220 via a connection 221 and aconnection 270. The aforementioned communication can take place betweenthe remote terminal 268 and “the cloud” which in the present descriptioncomprises at least the one server module 201. The remote terminal 268typically has input and output devices (not shown) which are similar tothose described in regard to the server module 201. The communicationsnetwork 220 may be a wide-area network (WAN), such as the Internet, acellular telecommunications network, or a private WAN. Where theconnection 221 is a telephone line, the modem 216 may be a traditional“dial-up” modem. Alternatively, where the connection 221 is a highcapacity (e.g., cable) connection, the modem 216 may be a broadbandmodem. A wireless modem may also be used for wireless connection to thecommunications network 220.

The computer server module 201 typically includes at least one processorunit 205, and a memory unit 206. For example, the memory unit 206 mayhave semiconductor random access memory (RAM) and semiconductor readonly memory (ROM). The remote terminal 268 typically includes as leastone processor 269 and a memory 272. The computer server module 201 alsoincludes a number of input/output (I/O) interfaces including: anaudio-video interface 207 that couples to the video display 214,loudspeakers 217 and microphone 280; an I/O interface 213 that couplesto the keyboard 202, mouse 203, scanner 226, camera 227 and optionally ajoystick or other human interface device (not illustrated); and aninterface 208 for the external modem 216 and printer 215. In someimplementations, the modem 216 may be incorporated within the computermodule 201, for example within the interface 208. The computer module201 also has a local network interface 211, which permits coupling ofthe computer system 200 via a connection 223 to a local-areacommunications network 222, known as a Local Area Network (LAN). Asillustrated in FIG. 1A, the local communications network 222 may alsocouple to the wide network 220 via a connection 224, which wouldtypically include a so-called “firewall” device or device of similarfunctionality. The local network interface 211 may include an Ethernetcircuit card, a Bluetooth® wireless arrangement or an IEEE 802.11wireless arrangement; however, numerous other types of interfaces may bepracticed for the interface 211.

The I/O interfaces 208 and 213 may afford either or both of serial orparallel connectivity; the former may be implemented according to theUniversal Serial Bus (USB) standards and having corresponding USBconnectors (not illustrated). Storage memory devices 209 are providedand typically include a hard disk drive (HDD) 210. Other storage devicessuch as a floppy disk drive and a magnetic tape drive (not illustrated)may also be used. An optical disk drive 212 is typically provided to actas a non-volatile source of data. Portable memory devices, such opticaldisks (e.g., CD-ROM, DVD, Blu ray Disc™), USB-RAM, portable, externalhard drives, and floppy disks, for example, may be used as appropriatesources of data to the system 200.

The components 205 to 213 of the computer module 201 typicallycommunicate via an interconnected bus 204 and in a manner that resultsin a conventional mode of operation of the computer system 200 known tothose in the relevant art. For example, the processor 205 is coupled tothe system bus 204 using a connection 218. Likewise, the memory 206 andoptical disk drive 212 are coupled to the system bus 204 by connections219.

The techniques described in this specification may be implemented usingthe computer system 200, e.g., may be implemented as one or moresoftware application programs 233 executable within the computer system200. In some implementations, the one or more software applicationprograms 233 execute on the computer server module 201 (the remoteterminal 268 may also perform processing jointly with the computerserver module 201), and a browser 271 executes on the processor 269 inthe remote terminal, thereby enabling a user of the remote terminal 268to access the software application programs 233 executing on the server201 (which is often referred to as “the cloud”) using the browser 271.In particular, the techniques described in this specification may beeffected by instructions 231 (see FIG. 1B) in the software 233 that arecarried out within the computer system 200. The software instructions231 may be formed as one or more code modules, each for performing oneor more particular tasks. The software may also be divided into twoseparate parts, in which a first part and the corresponding code modulesperforms the described techniques and a second part and thecorresponding code modules manage a user interface between the firstpart and the user.

The software may be stored in a computer readable medium, including thestorage devices described below, for example. The software is loadedinto the computer system 200 from the computer readable medium, and thenexecuted by the computer system 200. A computer readable medium havingsuch software or computer program recorded on the computer readablemedium is a computer program product. Software modules for that executetechniques described in this specification may also be distributed usinga Web browser.

The software 233 is typically stored in the HDD 210 or the memory 206(and possibly at least to some extent in the memory 272 of the remoteterminal 268). The software is loaded into the computer system 200 froma computer readable medium, and executed by the computer system 200.Thus, for example, the software 233, which can include one or moreprograms, may be stored on an optically readable disk storage medium(e.g., CD-ROM) 225 that is read by the optical disk drive 212. Acomputer readable medium having such software or computer programrecorded on it is a computer program product.

In some instances, the application programs 233 may be supplied to theuser encoded on one or more CD-ROMs 225 and read via the correspondingdrive 212, or alternatively may be read by the user from the networks220 or 222. Still further, the software can also be loaded into thecomputer system 200 from other computer readable media. Computerreadable storage media refers to any non-transitory tangible storagemedium that provides recorded instructions and/or data to the computersystem 200 for execution and/or processing. Examples of such storagemedia include floppy disks, magnetic tape, CD-ROM, DVD, Blu-Ray™ Disc, ahard disk drive, a ROM or integrated circuit, USB memory, amagneto-optical disk, or a computer readable card such as a PCMCIA cardand the like, whether or not such devices are internal or external ofthe computer module 201. Examples of transitory or non-tangible computerreadable transmission media that may also participate in the provisionof software, application programs, instructions and/or data to thecomputer module 201 include radio or infra-red transmission channels aswell as a network connection to another computer or networked device,and the Internet or Intranets including e-mail transmissions andinformation recorded on Websites and the like.

The second part of the application programs 233 and the correspondingcode modules mentioned above may be executed to implement one or moregraphical user interfaces (GUIs) to be rendered or otherwise representedupon the display 214. For example, through manipulation of the keyboard202 and the mouse 203, a user of the computer system 200 and theapplication may manipulate the interface in a functionally adaptablemanner to provide controlling commands and/or input to the applicationsassociated with the GUI(s). Other forms of functionally adaptable userinterfaces may also be implemented, such as an audio interface utilizingspeech prompts output via the loudspeakers 217 and user voice commandsinput via the microphone 280.

FIG. 1B is a detailed schematic block diagram of the processor 205 and a“memory” 234. The memory 234 represents a logical aggregation of all thememory modules (including the HDD 209 and semiconductor memory 206) thatcan be accessed by the computer module 201 in FIG. 1A.

When the computer module 201 is initially powered up, a power-onself-test (POST) program 250 can execute. The POST program 250 can bestored in a ROM 249 of the semiconductor memory 206 of FIG. 1A. Ahardware device such as the ROM 249 storing software is sometimesreferred to as firmware. The POST program 250 examines hardware withinthe computer module 201 to ensure proper functioning and typicallychecks the processor 205, the memory 234 (209, 206), and a basicinput-output systems software (BIOS) module 251, also typically storedin the ROM 249, for correct operation. Once the POST program 250 has runsuccessfully, the BIOS 251 can activate the hard disk drive 210 of FIG.1A. Activation of the hard disk drive 210 causes a bootstrap loaderprogram 252 that is resident on the hard disk drive 210 to execute viathe processor 205. This loads an operating system 253 into the RAMmemory 206, upon which the operating system 253 commences operation. Theoperating system 253 is a system level application, executable by theprocessor 205, to fulfil various high-level functions, includingprocessor management, memory management, device management, storagemanagement, software application interface, and generic user interface.

The operating system 253 manages the memory 234 (209, 206) to ensurethat each process or application running on the computer module 201 hassufficient memory in which to execute without colliding with memoryallocated to another process. Furthermore, the different types of memoryavailable in the system 200 of FIG. 1A must be used properly so thateach process can run effectively. Accordingly, the aggregated memory 234is not intended to illustrate how particular segments of memory areallocated (unless otherwise stated), but rather to provide a generalview of the memory accessible by the computer system 200 and how such isused.

As shown in FIG. 1B, the processor 205 includes a number of functionalmodules including a control unit 239, an arithmetic logic unit (ALU)240, and a local or internal memory 248, sometimes called a cachememory. The cache memory 248 typically includes a number of storageregisters 244-246 in a register section. One or more internal busses 241functionally interconnect these functional modules. The processor 205typically also has one or more interfaces 242 for communicating withexternal devices via the system bus 204, using a connection 218. Thememory 234 is coupled to the bus 204 using a connection 219.

The application program 233 includes a sequence of instructions 231 thatmay include conditional branch and loop instructions. The program 233may also include data 232 which is used in execution of the program 233.The instructions 231 and the data 232 are stored in memory locations228, 229, 230 and 235, 236, 237, respectively. Depending upon therelative size of the instructions 231 and the memory locations 228-230,a particular instruction may be stored in a single memory location asdepicted by the instruction shown in the memory location 230.Alternately, an instruction may be segmented into a number of parts eachof which is stored in a separate memory location, as depicted by theinstruction segments shown in the memory locations 228 and 229.

In general, the processor 205 is given a set of instructions which areexecuted therein. The processor 205 waits for a subsequent input, towhich the processor 205 reacts to by executing another set ofinstructions. Each input may be provided from one or more of a number ofsources, including data generated by one or more of the input devices202, 203, data received from an external source 101, e.g., a brainimaging device 101 such such as an MRI or DTI scanner, across one of thenetworks 220, 202, data retrieved from one of the storage devices 206,209 or data retrieved from a storage medium 225 inserted into thecorresponding reader 212, all depicted in FIG. 1A. The execution of aset of the instructions may in some cases result in output of data.Execution may also involve storing data or variables to the memory 234.

Some techniques described in this specification use input variables 254,e.g., data sets characterizing the brain of a patient, which are storedin the memory 234 in corresponding memory locations 255, 256, 257. Thetechniques can produce output variables 261, which are stored in thememory 234 in corresponding memory locations 262, 263, 264. Intermediatevariables 258 may be stored in memory locations 259, 260, 266 and 267.

Referring to the processor 205 of FIG. 1B, the registers 244, 245, 246,the arithmetic logic unit (ALU) 240, and the control unit 239 worktogether to perform sequences of micro-operations needed to perform“fetch, decode, and execute” cycles for every instruction in theinstruction set making up the program 233. Each fetch, decode, andexecute cycle can include i) a fetch operation, which fetches or readsan instruction 231 from a memory location 228, 229, 230; ii) a decodeoperation in which the control unit 239 determines which instruction hasbeen fetched; and iii) an execute operation in which the control unit239 and/or the ALU 240 execute the instruction.

Thereafter, a further fetch, decode, and execute cycle for the nextinstruction may be executed. Similarly, a store cycle may be performedby which the control unit 239 stores or writes a value to a memorylocation 232.

Each step or sub-process in the techniques described in thisspecification may be associated with one or more segments of the program233 and is performed by the register section 244, 245, 247, the ALU 240,and the control unit 239 in the processor 205 working together toperform the fetch, decode, and execute cycles for every instruction inthe instruction set for the noted segments of the program 233. Althougha cloud-based platform has been described for practicing the techniquesdescribed in this specification, other platform configurations can alsobe used. Furthermore, other hardware/software configurations anddistributions can also be used for practicing the techniques describedin this specification.

FIGS. 2-6 illustrate example graphical interfaces for displaying promptsto a user for determining a subset of brain data of a patient that isclinically relevant to the patient. FIG. 7 illustrates an examplegraphical interface for displaying the subset of the brain data to theuser. Data for each example graphical interface can be generated by acomputer server module, e.g., the computer server module 201 depicted inFIG. 1A and then forwarded to a display or remote terminal. Aftergraphical interface data is generated, the graphical interface can bepresented to the user on a display device, e.g., on the display device214 depicted in FIG. 1A. As described above, in some implementations thecomputer server module is in the same location as the display device,e.g., the computer server module and the display device can each becomponents of the same device. In some other implementations, thecomputer server module is remote, e.g., on the cloud.

In either case, the user can interact with the graphical interface inorder to provide responses to the prompts displayed on the displaydevice; for example, the user can provide one or more user inputs usingkeyboard, mouse, and/or microphone, e.g., the keyboard 202, mouse 203,and microphone 280 depicted in FIG. 1A. The user inputs can be providedto the computer server module, which can process the user inputs inorder to process the brain data and/or generate a next graphicalinterface for the user.

For convenience, in the descriptions below of FIGS. 2-7 , the graphicalinterfaces will be described as being generated and provided to the userby a “system,” i.e., a system of one or more computers located in one ormore locations. It is to be understood that the system can include thecomputer server module and the display device described above withreference to FIG. 1A.

In particular, given a particular sequence of prompts and responses, thesystem can determine either i) a next prompt to provide to the user, orii) a subset of the brain data of the patient to provide to the user. Inorder to make this determination, the system can maintain a data modelthat defines, for every possible sequence of prompts and responses,either a next prompt or a particular subset of patient brain data. As aparticular example, the system can maintain a tree structure, where eachnon-leaf node of the tree corresponds to a prompt, each branch of thetree corresponds to a particular response to a prompt, and each leafnode corresponds to a particular subset of patient brain data. When thesystem displays a first prompt to the user and receives back a response,the system can descend down the tree structure from the nodecorresponding to the first prompt, along the branch corresponding to theresponse, and to a second node corresponding to a second prompt. Thesystem can continue traversing the tree until reaching a leaf node, atwhich time the system can end the series of prompts and determine aparticular subset of the brain data of the patient. The system can useany appropriate data model to define how the system should proceed givena particular sequence of prompt responses.

While particular examples of prompts and series of prompts are presentedin FIGS. 2-6 , in general the system can present any prompt that mightbe relevant to generating a subset of the brain data of a patient. Inparticular, the prompts in a series of prompts can be presented in adifferent order than the example series of prompts that are depicted inFIGS. 2-6 , and a series of prompts can include one or more prompts thatare not depicted in FIGS. 2-6 at all.

FIG. 2 illustrates an example introductory series of prompts 290, 294,and 295.

The system can present the first prompt 290 to the user upon receiving auser input indicating that the user wishes to generate a new subset ofthe brain data of a patient. For example, the user might select an iconthat corresponds to starting a new flow of prompts. In someimplementations, the first prompt 290 can be preceded by a selection ofa particular patient of whose brain data the user wishes to generate asubset. In some other implementations, the user is already examiningdata related to a particular patient when the user submits the userinput that triggers the first prompt 290.

The first prompt 290 includes three options 291, 292, and 293 that theuser may select. The first option 291 allows a user to immediately beginto analyze the brain data of the patient. That is, the subset of thedata corresponding to the first option 291 is the entire set of braindata of the patient. This can be useful if the user does not knowexactly what the user might be looking for, and wishes to analyze thebrain data on a high level.

The second option 292 allows the user to plan a brain surgery, whichtriggers a series of one or more prompts related to planning the brainsurgery. If the user selects the second option 292, then the system cangenerate and display the second prompt 294, discussed in more detailbelow.

The third option 293 allows the user to enter a “research mode,” whichtriggers a series of one or more prompts related to particular braindiseases and mental disorders. In some implementations, the “researchmode” includes techniques that are not yet approved by a regulatoryagency. Some regulatory agencies allow some non-regulatory-approvedtechniques to be offered in applications to users as long as thetechniques are in a clearly indicated “research mode” in theapplication. As a particular example, a machine learning model thatprocesses brain data of a patient to generate a prediction of whetherthe patient has a particular brain disease may be provided to the useras a possibility if the user selects the third prompt 293 correspondingto the “research mode.”

The second prompt 294, which can be displayed to the user in response tothe user selecting the second option 292 of the first prompt 290, allowsthe user to select a particular region of the brain for which thepatient might undergo surgery. Note that the regions of the braindepicted in FIG. 2 are exemplary only and are not intended to be anexhaustive list.

In some cases, when the user selects a region of the brain in responseto the second prompt 294, the system ends the series of prompts andselects the subset of the brain data of the patient that corresponds tothe selected region of the brain. The system can then display thedetermined subset of brain data to the user. This process is discussedin more detail below in reference to FIG. 7 .

In some other cases, when the user selects a region of the brain, e.g.,the lateral frontal region, the system can generate and display a newprompt that lists possible types of brain surgeries that can beperformed in the selected region.

The third prompt 295, which can be displayed to the user in response tothe user selecting the third option 293 of the first prompt 290, allowsthe user to select a particular brain disease or mental disorder. Notethat the diseases depicted in FIG. 2 are exemplary only and are notintended to be an exhaustive list.

When the user selects a disease, the system can generate another promptcorresponding to the selected disease. FIGS. 3-6 each illustrate exampleseries of prompts corresponding to respective selected diseases.

FIG. 3 illustrates an example series of prompts 310-330 related to braintumors. That is, the system can generate and display the second prompt320 in response to the user selecting the “brain tumor” option in thefirst prompt 310.

The second prompt 320 provides the user a list of options forproceeding. For example, if the user selects the “plan using machinelearning” option, then the system can end the series of prompts andselect one or more machine learning models related to brain tumors. As aparticular example, a machine learning model can process a particularsubset of the brain data of the user to generate a recommendation for atreatment, e.g., surgery or chemotherapy. In some implementations, thesystem can display the one or more machine learning models to the userin a graphical interface, and the user can select one or more of themachine learning models to be executed. In some other implementations,the system can automatically execute the one or more machine learningmodels and display the model outputs of the one or more machine learningmodels.

As another example, if the user selects the “plan using resting state”option, then the system can end the series of prompts and select asubset of the brain data that was gathered while the user was in aresting state, e.g., a resting state fMRI of the user. The system canfurther subset the resting state data according to clinical research orone or more machine learning models that determine which subset ofresting state data is most informative with respect to brain tumors.

As another example, if the user selects the “study the connectome”option, then the system can end the series of prompts and select asubset of connectomics data in the brain data that is known to berelated to brain tumors.

As another example, if the user selects the “study the graph” option,then the system can end the series of prompts, select a subset of datain the brain data that is known to be related to brain tumors, andprocess the subset of data using one or more graph-theoretic models. Forexample, the system can process the determined subset of data usingmachine learning models that represent the brain as a graph of nodesconnected by edges and extract useful information using thisrepresentation.

As another example, if the user selects the “calculate a difference map”option, then the system can end the series of prompts, select a subsetof the brain data related to brain tumors, and generate a differencemap, which is a visual display of how connectomics features have changesover time. The system can then display the difference map to the user.

As another example, if the user selects the “select a neurologicaldeficit” option, then the system can generate and display the thirdprompt 330 to the user. The third prompts lists possible neurologicaldeficits that can be caused by a brain tumor. When the user selects oneof the options, e.g., the “language” option, the system can end theseries of prompts and determine a subset of the brain data thatcorresponds to the selected neurological deficit. That is, certainportions of the brain are known to relate to motor skills, language,vision, etc. Therefore, the system can determine the subset of the braindata that characterizes the region corresponding to the selectedneurological deficit. The system can then display the determined subsetof brain data to the user. This process is discussed in more detailbelow in reference to FIG. 7 .

FIG. 4 illustrates an example series of prompts 410-420 related todementia. That is, the system can generate and display the second prompt420 in response to the user selecting the “dementia” option in the firstprompt 410.

The second prompt 420 provides the user a list of options forproceeding. For example, if the user selects the “compare connectome tonormal” option, then the system can end the series of prompts anddetermine a subset of connectomics data in the brain data of the patientthat is known to be related to dementia. The system can also obtain a“normal” version of the determined connectomics data, e.g., an averagedversion of the determined connectomics data corresponding to differentpatients that are known not to have dementia. The system can also selectone or more machine learning algorithms that are trained to processconnectomics data corresponding to a particular patient and generate aprediction of whether the particular patient has dementia, e.g.,generate a value between 0 and 1 characterizing the likelihood that theparticular patient has dementia.

As another example, if the user selects the “classify subtype” option,then the system can end the series of prompts and determine a subset ofthe brain data of the patient that is known to relate to one or moresubtypes of dementia. The system can also select one or more machinelearning algorithms that are trained to process brain data of aparticular patient and generate a prediction of which subtype ofdementia the patient might have.

As another example, if the user selects the “address a neurologicaldeficit” option, then the system can generate and display a list ofpossible neurological deficits, as described above in reference to FIG.3 .

As another example, if the user selects the “study changes in thedisease” option, then the system can end the series of prompts anddetermine i) a first subset of the brain data corresponding to aparticular region of the brain and that was gathered at a first timepoint, and ii) a second subset of the brain data corresponding to theparticular region of the brain and that was gathered at a second timepoint. That is, the two subsets of the brain data correspond to the sameregion of the brain at different time points, e.g., two images of thebrain captured on two different dates. The system can display thesesubsets of data to the user, so that the user can compare the twosubsets and determine a change over time.

FIG. 5 illustrates an example series of prompts 510-530 related todepression. That is, the system can generate and display the secondprompt 520 in response to the user selecting the “depression” option inthe first prompt 510.

The second prompt 520 provides the user a list of options forproceeding. For example, if the user selects the “compare connectome tonormal” option, then the system can end the series of prompts anddetermine a subset of connectomics data in the brain data of the patientthat is known to be related to depression, as described above inreference to FIG. 4 .

As another example, if the user selects the “classify subtype” option,then the system can end the series of prompts and determine a subset ofthe brain data of the patient that is known to relate to one or moresubtypes of depression, as described above in reference to FIG. 4 .

As another example, if the user selects the “select a clinical statespecific model” option, then the system can display a new prompt to theuser to select a particular machine learning model that will process thebrain data. Each machine learning model can correspond to a particularclinical state, and can be trained to process a subset of the brain dataof the patient that is known to relate to depression to generate a modeloutput. For example, one or more of the machine learning models can betrained to determine how the patient might respond to a particulartreatment, e.g., transcranial magnetic stimulation (TMS) or a particularmedication. The system can then execute the selected machine learningmodel, and display the model outputs to the user.

As another example, if the user selects the “address a symptom” option,then the system can generate and display the third prompt 530 to theuser. The third prompts lists possible symptoms that are associated withdepression, e.g., psychomotor retardation, sleep disturbance, etc. Whenthe user selects one of the symptoms, then the system can end the seriesof prompts and determine a subset of the brain data of the patient thatis known to be related to depression, and in particular that is known tobe related to the selected symptom.

FIG. 6 illustrates an example series of prompts 610-620 related toepilepsy. That is, the system can generate and display the second prompt620 in response to the user selecting the “epilepsy” option in the firstprompt 610.

The second prompt 620 provides the user a list of options forproceeding. For example, if the user selects the “find the focus”option, then the system can end the sequence of prompts, determine asubset of the brain data that is known to correspond to epilepsy, andprocess the determined subset of the brain data using a machine learningmodel that is trained to identify abnormalities in one or more regionsof the brain that might suggest that the regions will be the onset zoneof seizures in the patient. As a particular example, the machinelearning model can be a convolutional neural network that processesconnectomics data of a patient to identify abnormalities in one or moreregions.

As another example, if the user selects the “determine what was removed”option, then the system can end the sequence of prompts, select a subsetof the brain data related to epilepsy, and generate a difference mapcharacterizing how the subset of brain data has changed over time. Forexample, the system can determine a first subset of the brain data thatwas obtained before an operation and a second subset of the brain datathat was obtained after the operation, and generate a difference mapcharacterizing the change in the brain data caused by the operation.FIG. 7 illustrates an example graphical interface 700 for presenting asubset of brain data of a patient. The subset of the brain data of thepatient was determined by the system according to a sequence of promptsprovided to a user and responses to the prompts obtained from the user.The graphical interface 700 can be displayed to the user after the finalprompt.

The graphical interface 700 includes a list 710 of the regions of thebrain, with the regions that correspond to the determined subset of thebrain data selected. In the example depicted in FIG. 7 , the “language”region is selected because the determined subset of the brain dataincludes data corresponding to the language region of the brain. As aparticular example, the system can generate the graphical interface 700and display it to the user in response to the user selecting the“language” option to the third prompt 330 depicted in FIG. 3 .

The other regions of the brain listed in the list 710 that are notselected each have a corresponding graphical element, depicted as anempty box, that the user can select. When the user selects a new region,the system can add data corresponding to the region to the subset of thebrain data and re-generate the graphical interface 700 to reflect theaugmented subset of the brain data.

The graphical interface 700 includes a correlation matrix 720 that is apart of the determined subset of the brain data of the patient. Acorrelation matrix can be generated from the brain functionalconnectivity data of a patient, and displays the correlation betweenareas of the brain of the patient. In particular, for an element of acorrelation matrix in a row that corresponds to a first area of thebrain and in a column that corresponds to a second region of the brain,the value of the element characterizes the correlation between the firstarea and the second area. The value of each element can be representedby a different color.

The correlation matrix 720 corresponds to the language region of thebrain of the patient. The interface 700 also includes a correlationmatrix 730 corresponding to a “normal” scan. For example, as describedabove, the “normal” correlation matrix 730 can be an averaged version ofthe correlation matrices of multiple different patients that are knownto have healthy language regions of the brain. The patient's correlationmatrix 720 and the normal correlation matrix 730 are presentedside-by-side so that the user can easily compare them.

The graphical interface 700 includes a three-dimensional model 740 ofdiffusion tractography data corresponding to a determined subset of thebrain data. In some implementations, the system only renders the tracts742 corresponding to the determined subset. Instead or in addition, thesystem can render the tracts 744 of the whole brain, and emphasize orhighlight the tracts 746 corresponding to the determined subset, e.g.,using a particular color.

The graphical interface 700 includes a list of selected machine learningmodels 750 that correspond to the determined subset of the brain data ofthe patient. As described above, the system can select the machinelearning models either using the same series of prompts by which thesystem determined the subset of brain data or via additional prompts.The selected machine learning models are clinically-relevant to thepatient, according to the responses to the prompts.

As a particular example, a selected machine learning model can betrained to process the subset of data corresponding to the languageregion of the brain and generate a prediction of whether the patient hasone or more diseases in the language region of the brain, e.g.,Alzheimer's. As another particular example, a selected machine learningmodel can be trained to process the subset of data and generate aprediction of whether the patient will respond to a particular treatmentor drug. As another particular example, a selected machine learningmodel can be trained to process the subset of data and prediction of oneor more regions within the brain that might be an epilepsy zone. Asanother particular example, a selected machine learning model can betrained to process the subset of data and generate a prediction of oneor more regions within the brain that might be affected by psychomotorretardation.

The machine learning models can each be trained to receive a model inputthat includes, at least a portion of the determined subset of the braindata, and process the model input to generate a model output. Forexample, one or more of the machine learning models can be trained toreceive a model input that is exactly equal to the determined subset ofthe brain data, while one or more of the machine learning models can betrained to receive a model input that includes i) only a portion of thedetermined subset of the brain data and ii) a different subset of thebrain data.

Each of the selected machine learning models in the list 750 have acorresponding graphical element, depicted as an arrow, that the user canselect. When the user selects a machine learning model, the system canexecute the machine learning model to generate a model output, anddisplay the model output to the user.

The graphical interface 700 includes an “Export selection” option 760that allows the user to export the determined subset of the brain dataof the patient. That is, if the user selects the option 760, then thesystem can compile the determined subset of the data into a single fileand provide the file to the user for download. Downloading the entireset of brain data of the user for further analysis can be intractable,as the amount of data can be too large for practical use; however,downloading only the relevant subset of the brain data, as determined bythe clinically-relevant questions described above, can be moremanageable and allow a user to further process the data.

As a particular example, a database that includes the brain data of thepatient can have a data item, e.g., a tensor, that identifiesthree-dimensional coordinates of each parcellation in the brain data ofthe patient, e.g., (x,y,z) coordinates in a common coordinate space ofthe brain data of the patient. The database can also include a dataitem, e.g., a tensor, that identifies three-dimensional coordinate ofeach tract in the brain data of the patient, e.g., (x,y,z) coordinatesin the common coordinate space. When exporting the determined subset ofthe brain data, the system can determine which data items to include inthe exported file using the respective (x,y,z) coordinates. In theexample depicted in FIG. 7 , the system can determine a region in thecommon coordinate system, e.g., a set of (x,y,z) coordinates in thecommon coordinate system, that corresponds to the “language region” ofthe brain. The system can then determine which data points have (x,y,z)coordinates that are within the defined region, and select those datapoints to include in the exported file.

FIG. 8 is a flowchart of an example process 800 for determining a subsetof brain data. The process 800 can be implemented by one or morecomputer programs installed on one or more computers and programmed inaccordance with this specification. For example, the process 800 can beperformed by the computer server module depicted in FIG. 1A. Forconvenience, the process 800 will be described as being performed by asystem of one or more computers.

The system obtains data characterizing the brain of a patient (step802). The obtained data can include connectomics data of the brain ofthe patient. For example, the connectomics data can include aconnectivity matrix that include multiple columns and rows correspondingto different regions of the brain.

The system determines a first prompt (step 804). For example, the firstprompt can be a prompt to select a brain region, a prompt to select adisease, a prompt to select symptoms of the patient, or a prompt toselect a neurological deficit. The system can provide the first promptfor display to the user, with one or more options for the user torespond.

The system obtains a first user input characterizing a response to thefirst prompt (step 806).

The system determines a second prompt (step 808). The system candetermine the second prompt according to the response to the firstprompt. As described above, the second prompt can be a prompt to selecta brain region, a prompt to select a disease, a prompt to selectsymptoms of the patient, or a prompt to select a neurological deficit.The system can provide the second prompt for display to the user, withone or more options for the user to respond.

The system obtains a second user input characterizing a response to thesecond prompt (step 810). Either or both of the response to the firstprompt or the response to the second prompt can seek a response based ona clinical observation of the patient.

The system determines a subset of the obtained data using the responsesto the first prompt and the second prompt (step 812). The subset of theobtained data can include a subset of the connectivity matrix, i.e., asubset of the columns and rows.

Optionally, system provides the determined subset of the obtained datafor display to the user (step 813). The system can display thedetermined subset to the user using a graphical interface. The graphicalinterface can include one or more user options to change the subset ofthe obtained data displayed on the graphical interface. That is, if theuser selects one of these options, the system can display a differentsubset of the obtained data. The graphical interface can also includeone or more user options to select a respective machine learning model.That is, if the user selects one of these options, the system canexecute the corresponding machine learning model to generate a modeloutput.

Optionally, the system selects a machine learning model using the firstresponse and the second response (step 814). The machine learning modelcan be configured through training to process brain data, e.g.,connectomics data, to generate a model output.

Optionally, the system processes at least a portion of the determinedsubset of the obtained data using the selected machine learning model togenerate a model output (step 816). For example, the model output cancharacterize a likelihood that the patient has a particular disease.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially-generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub-programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

For a system of one or more computers to be configured to performparticular operations or actions means that the system has installed onit software, firmware, hardware, or a combination of them that inoperation cause the system to perform the operations or actions. For oneor more computer programs to be configured to perform particularoperations or actions means that the one or more programs includeinstructions that, when executed by data processing apparatus, cause theapparatus to perform the operations or actions.

As used in this specification, an “engine,” or “software engine,” refersto a software implemented input/output system that provides an outputthat is different from the input. An engine can be an encoded block offunctionality, such as a library, a platform, a software development kit(“SDK”), or an object. Each engine can be implemented on any appropriatetype of computing device, e.g., servers, mobile phones, tabletcomputers, notebook computers, music players, e-book readers, laptop ordesktop computers, PDAs, smart phones, or other stationary or portabledevices, that includes one or more processors and computer readablemedia. Additionally, two or more of the engines may be implemented onthe same computing device, or on different computing devices.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and pointing device, e.g., a mouse, trackball, or a presencesensitive display or other surface by which the user can provide inputto the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents toand receiving documents from a device that is used by the user; forexample, by sending web pages to a web browser on a user's device inresponse to requests received from the web browser. Also, a computer caninteract with a user by sending text messages or other forms of messageto a personal device, e.g., a smartphone, running a messagingapplication, and receiving responsive messages from the user in return.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

In addition to the embodiments described above, the followingembodiments are also innovative:

Embodiment 1 is a method comprising:

obtaining data characterizing a brain of a patient;

determining a first prompt for presentation to a user;

obtaining a first user input characterizing a first response to thefirst prompt;

determining, using the first response to the first prompt, a secondprompt for presentation to the user;

obtaining a second user input characterizing a second response to thesecond prompt, wherein at least one of the first prompt or the secondprompt seek a response based on a clinical observation of the patient;and

determining a subset of the obtained data using the first response tothe first prompt and the second response to the second prompt.

Embodiment 2 is the method of embodiment 1, wherein the obtained datacomprises connectomics data.

Embodiment 3 is the method of embodiment 2, wherein the connectomicsdata comprises a connectivity matrix comprising a plurality of columnsand rows.

Embodiment 4 is the method of embodiment 3, wherein the subset of theobtained data comprises a subset of the plurality of columns and rows ofthe connectivity matrix.

Embodiment 5 is the method of any one of embodiments 1-4, wherein one ormore of the first prompt or the second prompt are one of:

a prompt to select a brain region;

a prompt to select a disease;

a prompt to select symptoms of the patient; or

a prompt to select a neurological deficit.

Embodiment 6 is the method of any one of embodiments 1-5, furthercomprising selecting, from the first response to the first prompt andthe second response to the second prompt, one or more machine learningmodels, wherein each machine learning model has been configured throughtraining to process data characterizing a brain of a patient andgenerate a model output.

Embodiment 7 is the method of embodiment 6, wherein the respective modeloutput of one or more of the machine learning models characterizes alikelihood that the patient has a particular disease.

Embodiment 8 is the method of any one of embodiments 1-7, furthercomprising providing the determined subset of the obtained data fordisplay to the user on a graphical interface.

Embodiment 9 is the method of embodiment 8, wherein the graphicalinterface comprises one or more of:

one or more user options to change the subset of the obtained datadisplayed on the graphical interface, or

one or more options to select a respective machine learning model.

Embodiment 10 is the method of any one of embodiments 1-9, furthercomprising generating a prediction of a health status of the patientaccording to the determined subset of the obtained data.

Embodiment 11 is a system comprising one or more computers and one ormore storage devices storing instructions that are operable, whenexecuted by the one or more computers, to cause the one or morecomputers to perform the method of any one of embodiments 1-10.

Embodiment 12 is a computer storage medium encoded with a computerprogram, the program comprising instructions that are operable, whenexecuted by data processing apparatus, to cause the data processingapparatus to perform the method of any one of embodiments 1-10.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain some cases, multitasking and parallel processing maybe advantageous.

What is claimed is:
 1. A method comprising: obtaining, by a system ofone or more computers, brain data characterizing a brain of a singleindividual under examination, wherein the brain data represents aplurality of regions of the brain of the single patient; determining afirst prompt for presentation to a user; providing the first prompt fordisplay to the user; obtaining a first user input characterizing a firstresponse to the first prompt; determining, based on the first responseto the first prompt, a second prompt for presentation to the user;providing the second prompt for display to the user; obtaining a seconduser input characterizing a second response to the second prompt,wherein at least one of the first prompt or the second prompt requiresthe user to provide an indication of a health status of the singleindividual; selecting, automatically by the system of one or morecomputers and using the first user input and the second user input, aproper subset of the brain data of the single individual, wherein theproper subset of the brain data represents a proper subset of theplurality of regions of the brain of the single individual, and whereinthe proper subset of the brain data is based at least in part on theindication of the health status of the single individual for acondition; in response to the automatic selection using the first userinput and the second user input, obtaining, automatically by the systemof computers, the proper subset of the brain data representing theproper subset of the plurality of regions without obtaining non-selectedportions of the brain data characterizing the brain of the individualunder examination, wherein the proper subset of the brain data is notmore than 1/100^(th) the size of the brain data; providing the obtainedproper subset of the brain data representing the proper subset of theplurality of regions of the brain of the single individual for displayto the user, wherein the proper subset of the brain data is displayed inan interface without displaying the non-selected portions of the braindata in the interface, wherein the brain data displayed comprises apatient connectivity matrix and a normal connectivity matrix, eachconnectivity matrix comprising a plurality of columns and rows, whereineach column and each row corresponds to a respective region of thebrain, the normal connectivity matrix being a function of theconnectivity matrices of patients that are known not to have thecondition, each connectivity matrix having at least 200 elements;selecting, in response to the first user input and the second userinput, one or more machine learning models, wherein each machinelearning model has been configured through training to process firstbrain data characterizing a brain of a first single patient and generatea model output representing a prediction about the brain of the firstsingle individual and processing the obtained proper subset of datausing the selected machine learning model to generate a model output. 2.The method of claim 1, wherein the brain data comprises connectomicsdata, the connectomics data comprising: for each pair of regionscomprising a first region and a second region from a plurality ofregions of the brain of the single patient, a degree of correlationbetween brain activity in the first region and brain activity in thesecond region.
 3. The method of claim 2, wherein the connectomics datacomprises data representing a connectivity matrix comprising a pluralityof columns and rows, wherein each column and each row corresponds to arespective region of the brain of the single patient.
 4. The method ofclaim 2, wherein the connectomics data comprises data representing aconnectivity matrix comprising a plurality of columns and rows, andwherein the proper subset of the brain data comprises a proper subset ofthe plurality of columns and rows of the connectivity matrix.
 5. Themethod of claim 1, wherein one or more of the first prompt or the secondprompt are one of: a prompt to select a brain region; a prompt to selecta disease of the single patient; a prompt to select one or more symptomsof the single patient; or a prompt to select a neurological deficit ofthe single patient.
 6. The method of claim 1, further comprisingselecting, in response to the first user input and the second userinput, one or more machine learning models, wherein each machinelearning model has been configured through training to process firstbrain data characterizing a brain of a first single patient and generatea model output representing a prediction about the brain of the firstsingle patient.
 7. The method of claim 6, wherein the respective modeloutputs generated by one or more of the machine learning modelscharacterize a likelihood that the single patient has a particulardisease.
 8. The method of claim 1, wherein displaying the proper subsetof the brain data in the interface comprises displaying one or more of:one or more user options to change the proper subset of the brain data,or one or more user options to select a machine learning model forprocessing the proper subset of the brain data.
 9. The method of claim1, further comprising generating a prediction about the health status ofthe single patient using the proper subset of the brain data.
 10. Themethod of claim 1, wherein determining the proper subset of the braindata characterizing the at least one specified network of the brain ofthe single patient comprises: determining coordinates, in athree-dimensional coordinate system of the brain of the single patient,of the at least one specified network; and identifying one or moreelements of the brain data that correspond to the determinedcoordinates.
 11. The method of claim 1, wherein: displaying the propersubset of the brain data in the interface comprises displaying adepiction of brain tracts of the at least one specified network of thebrain of the single patient, the depiction being generated from theproper subset of the brain data.
 12. A system comprising one or morecomputers and one or more storage devices storing instructions that areoperable, when executed by the one or more computers, to cause the oneor more computers to perform operations comprising: Obtaining, by asystem of one or more computers, brain data characterizing a brain of asingle individual under examination, wherein the brain data represents aplurality of regions of the brain of the single patient; determining afirst prompt for presentation to a user; providing the first prompt fordisplay to the user; obtaining a first user input characterizing a firstresponse to the first prompt; determining, based on the first responseto the first prompt, a second prompt for presentation to the user;providing the second prompt for display to the user; obtaining a seconduser input characterizing a second response to the second prompt,wherein at least one of the first prompt or the second prompt requiresthe user to provide an indication of a health status of the singleindividual; selecting, automatically by the system of one or morecomputers and using the first user input and the second user input, aproper subset of the brain data of the single individual, wherein theproper subset of the brain data represents a proper subset of theplurality of regions of the brain of the single individual, and whereinthe proper subset of the brain data is based at least in part on theindication of the health status of the single individual for acondition; in response to the automatic selection using the first userinput and the second user input, obtaining, automatically by the systemof computers, the proper subset of the brain data representing theproper subset of the plurality of regions without obtaining non-selectedportions of the brain data characterizing the brain of the individualunder examination, wherein the proper subset of the brain data is notmore than 1/100^(th) the size of the brain data; providing the obtainedproper subset of the brain data representing the proper subset of theplurality of regions of the brain of the single individual for displayto the user, wherein the proper subset of the brain data is displayed inan interface without displaying the non-selected portions of the braindata in the interface, wherein the brain data displayed comprises apatient connectivity matrix and a normal connectivity matrix, eachconnectivity matrix comprising a plurality of columns and rows, whereineach column and each row corresponds to a respective region of thebrain, the normal connectivity matrix being a function of theconnectivity matrices of patients that are known not to have thecondition, each connectivity matrix having at least 200 elements;selecting, in response to the first user input and the second userinput, one or more machine learning models, wherein each machinelearning model has been configured through training to process firstbrain data characterizing a brain of a first single patient and generatea model output representing a prediction about the brain of the firstsingle individual and processing the obtained proper subset of datausing the selected machine learning model to generate a model output.13. The system of claim 12, wherein: the brain data comprisesconnectomics data, the connectomics data comprising: for each pair ofregions comprising a first region and a second region from a pluralityof regions of the brain of the single patient, a degree of correlationbetween brain activity in the first region and brain activity in thesecond region.
 14. The system of claim 12, wherein one or more of thefirst prompt or the second prompt are one of: a prompt to select a brainregion; a prompt to select a disease of the single patient; a prompt toselect one or more symptoms of the single patient; or a prompt to selecta neurological deficit of the single patient.
 15. The system of claim12, wherein the operations further comprise selecting, in response tothe first user input and the second user input, one or more machinelearning models, wherein each machine learning model has been configuredthrough training to process first brain data characterizing a brain of afirst single patient and generate a model output representing aprediction about the brain of the first single patient.
 16. One or morenon-transitory storage media storing instructions that when executed byone or more computers cause the one or more computers to performoperations comprising: Obtaining, by a system of one or more computers,brain data characterizing a brain of a single individual underexamination, wherein the brain data represents a plurality of regions ofthe brain of the single patient; determining a first prompt forpresentation to a user; providing the first prompt for display to theuser; obtaining a first user input characterizing a first response tothe first prompt; determining, based on the first response to the firstprompt, a second prompt for presentation to the user; providing thesecond prompt for display to the user; obtaining a second user inputcharacterizing a second response to the second prompt, wherein at leastone of the first prompt or the second prompt requires the user toprovide an indication of a health status of the single individual;selecting, automatically by the system of one or more computers andusing the first user input and the second user input, a proper subset ofthe brain data of the single individual, wherein the proper subset ofthe brain data represents a proper subset of the plurality of regions ofthe brain of the single individual, and wherein the proper subset of thebrain data is based at least in part on the indication of the healthstatus of the single individual for a condition; in response to theautomatic selection using the first user input and the second userinput, obtaining, automatically by the system of computers, the propersubset of the brain data representing the proper subset of the pluralityof regions without obtaining non-selected portions of the brain datacharacterizing the brain of the individual under examination, whereinthe proper subset of the brain data is not more than 1/100^(th) the sizeof the brain data; providing the obtained proper subset of the braindata representing the proper subset of the plurality of regions thebrain of the single individual for display to the user, wherein theproper subset of the brain data is displayed in an interface withoutdisplaying the non-selected portions of the brain data in the interfacewherein the brain data displayed comprises a patient connectivity matrixand a normal connectivity matrix, each connectivity matrix comprising aplurality of columns and rows, wherein each column and each rowcorresponds to a respective region of the brain, the normal connectivitymatrix being a function of the connectivity matrices of patients thatare known not to have the condition, each connectivity matrix having atleast 200 elements; selecting, in response to the first user input andthe second user input, one or more machine learning models, wherein eachmachine learning model has been configured through training to processfirst brain data characterizing a brain of a first single patient andgenerate a model output representing a prediction about the brain of thefirst single individual and processing the obtained proper subset ofdata using the selected machine learning model to generate a modeloutput.
 17. The non-transitory storage media of claim 16, wherein: thebrain data comprises connectomics data, the connectomics datacomprising: for each pair of regions comprising a first region and asecond region from a plurality of regions of the brain of the singlepatient, a degree of correlation between brain activity in the firstregion and brain activity in the second region.
 18. The non-transitorystorage media of claim 16, wherein one or more of the first prompt orthe second prompt are one of: a prompt to select a brain region; aprompt to select a disease of the single patient; a prompt to select oneor more symptoms of the single patient; or a prompt to select aneurological deficit of the single patient.
 19. The non-transitorystorage media of claim 16, wherein the operations further compriseselecting, in response to the first user input and the second userinput, one or more machine learning models, wherein each machinelearning model has been configured through training to process firstbrain data characterizing a brain of a first single patient and generatea model output representing a prediction about the brain of the firstsingle patient.